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Machine learning risk models
Kakushadze, Zura; Yu, Willie - In: Journal of risk & control 6 (2019) 1, pp. 37-64
Persistent link: https://www.econbiz.de/10012237599
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Predicting daily surgical volumes using probabilistic estimates of providers' future availability
Eun, Joonyup; Tiwari, Vikram; Sandberg, Warren S. - In: Decision sciences 53 (2022) 1, pp. 124-149
Persistent link: https://www.econbiz.de/10013164978
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Nonparametric monitoring of multivariate data via KNN learning
Li, Wendong; Zhang, Chi; Tsung, Fugee; Mei, Yajun - In: International journal of production research 59 (2021) 20, pp. 6311-6326
Persistent link: https://www.econbiz.de/10012652733
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A probabilistic graphical models approach to model interconnectedness
Denev, Alexander; Papaioannou, Adrien; Angelini, Orazio - In: International journal of risk assessment and management … 23 (2020) 2, pp. 119-133
Persistent link: https://www.econbiz.de/10012256731
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Research on statistical characteristics modeling of matching probability and measurement error based on machine learning
Li, Shuan-zhu; He, Run-feng; Pan, Bao-zhu - In: International journal of information systems in the … 14 (2022) 2, pp. 1-13
Persistent link: https://www.econbiz.de/10014291926
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Identification of nonlinear determinants of stock indices derived by Random Forest algorithm
Tratkowski, Grzegorz - In: International journal of management and economics 56 (2020) 3, pp. 209-217
In this paper, the use of the machine learning algorithm is examined in derivation of the determinants of price movements of stock indices. The Random Forest algorithm was selected as an ideal representative of the nonlinear algorithms based on decision trees. Various brokering and investment...
Persistent link: https://www.econbiz.de/10012303034
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Learning dispatching rules using random forest in flexible job shop scheduling problems
Jun, Sungbum; Lee, Seokcheon; Chun, Hyonho - In: International journal of production research 57 (2019) 10, pp. 3290-3310
Persistent link: https://www.econbiz.de/10012193259
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Dynamic Factor Trees and Forests : a theory-led machine learning framework for non-linear and state-dependent short-term U.S. GDP growth predictions
Wochner, Daniel - 2020 - This version: January 31, 2020
theory (cf. e.g. Mukherjee, 2017; Veltri, 2017). This article proposes so-called Dynamic Factor Trees (DFT) and Dynamic … reduce to the standard Dynamic Factor Model (DFM) as a special case and allow us to embed theory-led factor models in …
Persistent link: https://www.econbiz.de/10012172506
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Constrained optimization of objective functions determined from random forests
Biggs, Max; Hariss, Rim; Perakis, Georgia - In: Production and operations management : the flagship … 32 (2023) 2, pp. 397-415
Persistent link: https://www.econbiz.de/10014266041
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Probabilistic recalibration of forecasts
Graziani, Carlo; Rosner, Robert; Adams, Jennifer M.; … - In: International journal of forecasting 37 (2021) 1, pp. 1-27
Persistent link: https://www.econbiz.de/10012692513